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Coforge Quasar MLVeins is an open source based accelerator that aims to deploy and maintain machine learning models in production reliably and efficiently. It is used for interaction between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm to production systems. MLVeins applies to the entire lifecycle - from integrating with model generation, orchestration, deployment, governance, and metrics. It's easy to use user interface makes working on ML projects quick and simple. User can solve problems related to regression,classification, clustering, recommendation and many more with their choice of different ML algorithms.

HOW WE TRANSFORM

Service offerings

Predictive Ancillary Product Offerings
Predictive Ancillary Product Offerings
With increasing competition, reduced profit margins on transporting cargo and passengers, airlines around the world are focusing on ancillary revenue streams to boost their bottom lines while optimizing costs and providing their customers a better travel experience. No frills airlines are relying more on ancillary revenues than on airfare or cargo charges to boost their revenues and profits.
Predictive Company Outlook using Financial Report
Predictive Company Outlook using Financial Report
Financial services industry operations involve lengthy financial and legal documents such as annual reports, contracts, product prospectus, research reports etc. Financial sentiments helps us in understanding the industry segment more accurately.
Prescription Analytics for customer issues
Prescription Analytics for customer issues
A Large Travel Organization in UK was looking at automation of their Contact Center operations. Solution is showing how a unified single interface can be used by the agent as against accessing multiple applications / systems / terminals etc. thereby impacting AHT and operational efficiency. AI is supporting email channels – segmentation / categorization, routing and insights.
Predictive Money Laundering using customer behavior
Predictive Money Laundering using customer behavior
Ever increasing AML activities and decreasing detection rates. Customers are complex entities and need continuous monitoring for anomalous behavior. Transaction rules are not sufficient. Traditional clustering using KYC details and behavior patterns helped predicting likely money laundering cases.
Predictive Ancillary Product Offerings
Predictive Ancillary Product Offerings
With increasing competition, reduced profit margins on transporting cargo and passengers, airlines around the world are focusing on ancillary revenue streams to boost their bottom lines while optimizing costs and providing their customers a better travel experience. No frills airlines are relying more on ancillary revenues than on airfare or cargo charges to boost their revenues and profits.
Predictive Company Outlook using Financial Report
Predictive Company Outlook using Financial Report
Financial services industry operations involve lengthy financial and legal documents such as annual reports, contracts, product prospectus, research reports etc. Financial sentiments helps us in understanding the industry segment more accurately.
Prescription Analytics for customer issues
Prescription Analytics for customer issues
A Large Travel Organization in UK was looking at automation of their Contact Center operations. Solution is showing how a unified single interface can be used by the agent as against accessing multiple applications / systems / terminals etc. thereby impacting AHT and operational efficiency. AI is supporting email channels – segmentation / categorization, routing and insights.
Predictive Money Laundering using customer behavior
Predictive Money Laundering using customer behavior
Ever increasing AML activities and decreasing detection rates. Customers are complex entities and need continuous monitoring for anomalous behavior. Transaction rules are not sufficient. Traditional clustering using KYC details and behavior patterns helped predicting likely money laundering cases.
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Information extraction from scanned manufacturing diagrams using Artificial Intelligence (AI) and Machine Learning (ML)
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